diff --git a/bayeux/_src/shared.py b/bayeux/_src/shared.py
index 514a80a..97d3355 100644
--- a/bayeux/_src/shared.py
+++ b/bayeux/_src/shared.py
@@ -33,7 +33,7 @@ def map_fn(chain_method, fn):
   elif chain_method == "vectorized":
     return jax.vmap(fn)
   elif chain_method == "sequential":
-    return functools.partial(jax.tree_map, fn)
+    return functools.partial(jax.tree.map, fn)
   raise ValueError(f"Chain method {chain_method} not supported.")
 
 
diff --git a/bayeux/_src/vi/tfp.py b/bayeux/_src/vi/tfp.py
index c8a6d78..2bb5156 100644
--- a/bayeux/_src/vi/tfp.py
+++ b/bayeux/_src/vi/tfp.py
@@ -30,7 +30,7 @@ class Custom(tfb.Bijector):
 
   def __init__(self, bx_model):
     super().__init__(
-        forward_min_event_ndims=jax.tree_map(jnp.ndim, bx_model.test_point))
+        forward_min_event_ndims=jax.tree.map(jnp.ndim, bx_model.test_point))
     self.bx_model = bx_model
 
   def _forward(self, x):
@@ -46,12 +46,12 @@ def _forward_log_det_jacobian(self, x):
     return -self.inverse_log_det_jacobian(self.forward(x))
 
   def _forward_event_shape_tensor(self, input_shape):
-    return jax.tree_map(jnp.shape,
-                        self._forward(jax.tree_map(jnp.ones, input_shape)))
+    return jax.tree.map(jnp.shape,
+                        self._forward(jax.tree.map(jnp.ones, input_shape)))
 
   def _inverse_event_shape_tensor(self, output_shape):
-    return jax.tree_map(jnp.shape,
-                        self._inverse(jax.tree_map(jnp.ones, output_shape)))
+    return jax.tree.map(jnp.shape,
+                        self._inverse(jax.tree.map(jnp.ones, output_shape)))
 
 
 def get_fit_kwargs(log_density, kwargs):
@@ -104,7 +104,7 @@ def get_kwargs(self, **kwargs):
     return {
         tfp.experimental.vi.build_factored_surrogate_posterior_stateless: (
             get_build_kwargs(
-                jax.tree_map(jnp.shape, self.test_point),
+                jax.tree.map(jnp.shape, self.test_point),
                 self.constraining_bijector(),
                 kwargs)),
         tfp.vi.fit_surrogate_posterior_stateless: get_fit_kwargs(
@@ -140,7 +140,7 @@ def __call__(self, seed, **kwargs):
     elif chain_method == "parallel":
       mapped_fit = jax.pmap(fit_fn)
     elif chain_method == "sequential":
-      mapped_fit = functools.partial(jax.tree_map, fit_fn)
+      mapped_fit = functools.partial(jax.tree.map, fit_fn)
     else:
       raise ValueError(f"Chain method {chain_method} not supported.")
 
diff --git a/docs/examples/numpyro_and_bayeux.ipynb b/docs/examples/numpyro_and_bayeux.ipynb
index 0dd66a9..a793b2e 100644
--- a/docs/examples/numpyro_and_bayeux.ipynb
+++ b/docs/examples/numpyro_and_bayeux.ipynb
@@ -637,7 +637,7 @@
         "ax.plot(losses.T)\n",
         "\n",
         "draws = surrogate_posterior.sample(100, seed=jax.random.PRNGKey(1))\n",
-        "jax.tree_map(lambda x: np.mean(x, axis=(0, 1)), draws)"
+        "jax.tree.map(lambda x: np.mean(x, axis=(0, 1)), draws)"
       ]
     }
   ],
diff --git a/docs/examples/pymc_and_bayeux.ipynb b/docs/examples/pymc_and_bayeux.ipynb
index cd4dcd8..a83071c 100644
--- a/docs/examples/pymc_and_bayeux.ipynb
+++ b/docs/examples/pymc_and_bayeux.ipynb
@@ -425,7 +425,7 @@
      "ax.plot(losses.T)\n",
      "\n",
      "draws = surrogate_posterior.sample(100, seed=jax.random.PRNGKey(1))\n",
-     "jax.tree_map(lambda x: np.mean(x, axis=(0, 1)), draws)"
+     "jax.tree.map(lambda x: np.mean(x, axis=(0, 1)), draws)"
     ]
    }
   ],
diff --git a/docs/examples/tfp_and_bayeux.ipynb b/docs/examples/tfp_and_bayeux.ipynb
index d62abbf..f58c738 100644
--- a/docs/examples/tfp_and_bayeux.ipynb
+++ b/docs/examples/tfp_and_bayeux.ipynb
@@ -631,7 +631,7 @@
         "ax.plot(losses.T)\n",
         "\n",
         "draws = surrogate_posterior.sample(100, seed=draw_key)\n",
-        "jax.tree_map(lambda x: np.mean(x, axis=(0, 1)), draws)"
+        "jax.tree.map(lambda x: np.mean(x, axis=(0, 1)), draws)"
       ]
     }
   ],